Article Dans Une Revue Gut Année : 2020

Artificial intelligence-guided tissue analysis combined with immune infiltrate assessment predicts stage III colon cancer outcomes in PETACC08 study

Cynthia Reichling
  • Fonction : Auteur
Julien Taieb
Valentin Derangere
  • Fonction : Auteur
Karine Le Malicot
  • Fonction : Auteur
Jean-Marc Gornet
  • Fonction : Auteur
Hakim Becheur
  • Fonction : Auteur
Francis Fein
  • Fonction : Auteur
Oana Cojocarasu
  • Fonction : Auteur
Marie Christine Kaminsky
  • Fonction : Auteur
Jean Paul Lagasse
  • Fonction : Auteur
Dominique Luet
  • Fonction : Auteur
Suzanne Nguyen
  • Fonction : Auteur
Pierre-Luc Etienne
  • Fonction : Auteur
Mohamed Gasmi
  • Fonction : Auteur
Andre Vanoli
  • Fonction : Auteur
Hervé Perrier
  • Fonction : Auteur
Pierre-Laurent Puig
  • Fonction : Auteur
Jean-François Emile
Come Lepage
  • Fonction : Auteur
François Ghiringhelli

Résumé

Objective Diagnostic tests, such as Immunoscore, predict prognosis in patients with colon cancer. However, additional prognostic markers could be detected on pathological slides using artificial intelligence tools. Design We have developed a software to detect colon tumour, healthy mucosa, stroma and immune cells on CD3 and CD8 stained slides. The lymphocyte density and surface area were quantified automatically in the tumour core (TC) and invasive margin (IM). Using a LASSO algorithm, DGMate (DiGital tuMor pArameTErs), we detected digital parameters within the tumour cells related to patient outcomes. Results Within the dataset of 1018 patients, we observed that a poorer relapse-free survival (RFS) was associated with high IM stromal area (HR 5.65; 95% CI 2.34 to 13.67; p<0.0001) and high DGMate (HR 2.72; 95% CI 1.92 to 3.85; p<0.001). Higher CD3+ TC, CD3+ IM and CD8+ TC densities were significantly associated with a longer RFS. Analysis of variance showed that CD3+ TC yielded a similar prognostic value to the classical CD3/CD8 Immunoscore (p=0.44). A combination of the IM stromal area, DGMate and CD3, designated ‘DGMuneS’, outperformed Immunoscore when used in estimating patients’ prognosis (C-index=0.601 vs 0.578, p=0.04) and was independently associated with patient outcomes following Cox multivariate analysis. A predictive nomogram based on DGMuneS and clinical variables identified a group of patients with less than 10% relapse risk and another group with a 50% relapse risk. Conclusion These findings suggest that artificial intelligence can potentially improve patient care by assisting pathologists in better defining stage III colon cancer patients’ prognosis.
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hal-03244957 , version 1 (06-01-2025)

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Cynthia Reichling, Julien Taieb, Valentin Derangere, Quentin Klopfenstein, Karine Le Malicot, et al.. Artificial intelligence-guided tissue analysis combined with immune infiltrate assessment predicts stage III colon cancer outcomes in PETACC08 study. Gut, 2020, 69 (4), pp.681-690. ⟨10.1136/gutjnl-2019-319292⟩. ⟨hal-03244957⟩

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